在无人机获取的甚高分辨率可见光图像中识别城市化区域

IF 4.2 2区 地球科学 Q2 ENVIRONMENTAL SCIENCES Remote Sensing Pub Date : 2024-09-17 DOI:10.3390/rs16183444
Edyta Puniach, Wojciech Gruszczyński, Paweł Ćwiąkała, Katarzyna Strząbała, Elżbieta Pastucha
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引用次数: 0

摘要

本研究比较了基于无人机获取的 RGB 图像区分城市化地区和非城市化地区的分类器。测试的解决方案包括多种植被指数(VI)阈值法和神经网络(NN)。分析针对两个研究区域进行,使用了不同的无人机和相机进行勘测。研究区域的地面取样距离分别为 10 毫米和 15 毫米。参考分类以人工方式进行,第一个区域获得约 2 400 万个分类像素,第二个区域获得约 380 万个分类像素。这项研究包括分析季节对测试 VI 的阈值的影响,以及作为 NN 输入的图像片段大小对分类准确性的影响。研究结果表明,与测试的最佳 VI(即 "过度蓝")(约 87%)相比,使用 NN 的分类准确率更高(约 96%)。由于所使用数据集的高度不平衡(非城市化地区约占数据集总数的 87%),马修斯相关系数也被用来评估分类的正确性。对分类结果的定性评估对基于统计测量的分析进行了补充,从而确定了阈值分类法和导航网分类法之间分类差异的最重要来源。
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Recognition of Urbanized Areas in UAV-Derived Very-High-Resolution Visible-Light Imagery
This study compared classifiers that differentiate between urbanized and non-urbanized areas based on unmanned aerial vehicle (UAV)-acquired RGB imagery. The tested solutions included numerous vegetation indices (VIs) thresholding and neural networks (NNs). The analysis was conducted for two study areas for which surveys were carried out using different UAVs and cameras. The ground sampling distances for the study areas were 10 mm and 15 mm, respectively. Reference classification was performed manually, obtaining approximately 24 million classified pixels for the first area and approximately 3.8 million for the second. This research study included an analysis of the impact of the season on the threshold values for the tested VIs and the impact of image patch size provided as inputs for the NNs on classification accuracy. The results of the conducted research study indicate a higher classification accuracy using NNs (about 96%) compared with the best of the tested VIs, i.e., Excess Blue (about 87%). Due to the highly imbalanced nature of the used datasets (non-urbanized areas constitute approximately 87% of the total datasets), the Matthews correlation coefficient was also used to assess the correctness of the classification. The analysis based on statistical measures was supplemented with a qualitative assessment of the classification results, which allowed the identification of the most important sources of differences in classification between VIs thresholding and NNs.
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来源期刊
Remote Sensing
Remote Sensing REMOTE SENSING-
CiteScore
8.30
自引率
24.00%
发文量
5435
审稿时长
20.66 days
期刊介绍: Remote Sensing (ISSN 2072-4292) publishes regular research papers, reviews, letters and communications covering all aspects of the remote sensing process, from instrument design and signal processing to the retrieval of geophysical parameters and their application in geosciences. Our aim is to encourage scientists to publish experimental, theoretical and computational results in as much detail as possible so that results can be easily reproduced. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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